Job description 

What success looks like in this role:

As a Generative AI Lead Engineer, you will be at the forefront of advancing artificial intelligence technology. Your primary responsibility will be to lead a team of highly skilled engineers, researchers, and developers in the design, development, and deployment of state-of-the-art generative AI models and applications. Your leadership will be instrumental in driving innovation and pushing the boundaries of what AI can achieve in content generation, creativity, and problem-solving.

Team Leadership:

• You will be tasked with not only managing your team but also inspiring and guiding them to excel in their roles. Your leadership will be essential in fostering a collaborative and innovative environment where ideas flow freely, and team members are motivated to push the envelope of what generative AI can do.

Research and Development:

• Staying at the cutting edge of AI research is a key part of your role. You will lead research initiatives to explore new techniques, algorithms, and approaches in generative AI, ensuring that your team remains at the forefront of this rapidly evolving field. Your work will contribute to breakthroughs in AI technology.

Project Management:

• You will oversee multiple projects concurrently, managing timelines, resources, and budgets. Collaboration with cross-functional teams, including data scientists, software engineers, and product managers, will be crucial to define project goals and ensure successful execution.

Algorithm Optimization:

• Efficiency, scalability, and performance are paramount in generative AI. You will be responsible for optimizing generative models, ensuring they are both powerful and efficient. Rigorous testing and debugging will be necessary to maintain the reliability and robustness of these models.

Quality Assurance:

• Your role will include establishing and upholding rigorous quality standards for generative AI outputs. This will involve developing testing and validation procedures to guarantee the high quality of generated content or solutions.

Documentation and Reporting:

• Clear documentation of research findings, algorithms, and code is essential for both internal and external stakeholders. You will prepare comprehensive reports and presentations to communicate your team's progress and findings effectively.

Collaboration and Communication:

• Collaboration is at the heart of this role. You will work closely with cross-functional teams, translating complex technical concepts into understandable insights for both technical and non-technical colleagues.

Compliance and Ethics:

• Ensuring that generative AI systems adhere to ethical guidelines and legal requirements is a vital aspect of this role, especially when working in areas with sensitive applications. Your leadership in ethical AI practices will be essential.

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You will be successful in this role if you have:

Qualifications:

• Your educational background should ideally include a Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• A minimum of 8-10 years of experience within the engineering organization with high-level expertise
• Proven hands-on experience in generative AI, deep learning, and natural language processing is a must, showcasing your expertise in the field.
• Strong hands-on programming skills in languages like Python, TensorFlow, PyTorch, or similar are necessary to lead technical teams effectively.
• Experience in leading and managing technical teams is vital, as you'll be responsible for guiding your team to success.
• Strong problem-solving and analytical skills will aid you in addressing complex challenges.
• Excellent communication and presentation skills are essential to convey your ideas and findings effectively.
• Knowledge of ethical AI principles and best practices is crucial to ensure responsible AI development.
• Your adaptability to evolving technologies and research is important in keeping your team at the forefront of generative AI advancements.

Preferred Qualifications:

• If you have published research in relevant AI conferences and journals, it demonstrates your contributions to the field.
• Certification in AI domain will be added advantage.
• Experience with cloud-based AI services and platforms showcases your familiarity with modern AI infrastructure.
• Familiarity with generative adversarial networks (GANs), recurrent neural networks (RNNs), and transformers indicates your depth of knowledge in generative AI techniques.
• Experience in working with large datasets and distributed computing is a valuable asset.
• Knowledge of industry-specific applications for generative AI, such as content generation, creative design, or natural language generation, can be a significant advantage in certain roles.